Data scientists
Resources for data scientists who want to boost their machine learning models with external data.
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Read the blogBias or Variance? How Each Affects Your Model and Why You Should Care
We explain the difference between machine learning bias and variance, why it’s so hard to fix both at once, and how they can impact your model.
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Read the blogWhat is Feature Engineering?
What is feature engineering, and why should you automate it? In this blog post, we answer these questions and more.
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Read nowThe Most Common Errors in ML Projects and How to Avoid Them
In this whitepaper, we cover some of the most common errors in ML initiatives, and best practices to avoid them
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Read the blogThe Data Science Buzzwords and Acronyms That Defined 2020
In the spirit of the new year, let’s take a look back at all the acronyms, buzzwords, and terms that dominated data science in 2020.
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Read the blogBest of the Best: Our Top 10 Blog Posts of 2020
Before we turn the page on 2020, let’s look back at our top ten blogs of the year that was.
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Read now2020 Data Science Review (and What to Expect in 2021)
In this 2020 wrap-up, we polled our in-house experts, drawing together their tips and insights for the end of the year and for what's to come in 2021.
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46:39Watch videoWebinar: Find and Use the Data You Need With Augmented Data Discovery
In this on-demand webinar, learn how you can use Explorium for augmented data discovery and connect to the data you need for better business insights.
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Read the blogHow Explorium Upgrades Your Data Pipeline
Building a sustainable data pipeline is critical to accurate machine learning models. Learn how Explorium supports you at every point in the process.
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Read the blog4 Ways To Know If You’re Using the Right Data Preparation Tools
In this blog post, we look at the key questions you need to ask to make sure you’re using the data preparation tools you really need.
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Read the blogMachine Learning in Retail: Building Smarter Inventory Models
See how machine learning in retail can help you build better inventory management systems.
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Read nowHow to Improve Your Training Data for Vastly Better Machine Learning
Making your training data better is much easier than you think, and you can use several easy strategies for quick wins.
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Read the blog4 Steps You Must Take to Prepare for Predictive Model Deployment
In this article, we explain how to get ready for predictive model deployment, from preparing data pipelines to retraining ML models.
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Read the blogWhat Is Augmented Data Discovery with Explorium?
With so much data in your own stores, it’s tempting to think you have all you need to start producing great predictive insights. This might be The post What Is Augmented Data Discovery with...
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Read the blogTop Tips for Data Preparation Using Python
Your machine learning model is only as good as the data you feed into it. That makes data preparation (or cleaning, wrangling, cleansing, pre-processing, or any The post Top Tips for Data...
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Read nowHow to Deploy and Future-Proof Your Models: From Theory to Production
It’s no secret that while most organizations understand the importance of machine learning, most initiatives never make it off the ground. Follow this guide to guarantee you make it to production.
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Read the blogDomain Knowledge in Data Science: Are Your Models Ready for Business?
In this in-depth article, we explain how the right questions will help you get the domain knowledge you need for data science for business.
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Read the blogHow Will Data Privacy Look in The Future?
Data privacy continues to be a major hurdle for risk officers. In this article, we explain how the global increase in data surveillance creates short term opportunities but long term risks.
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Read the blogThe Three Skills You Need to Instill in Your Data Science Team
To succeed in the data science field, you need more than just technical acumen. We reveal the secrets to making your team indispensable.
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Read the blogWhy You Need Data Catalogs, Not Databases
When it comes to external data for machine learning, data catalogs provide a handful of time-saving benefits over databases. Learn more.
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Read nowAI is Making BI Obsolete, and Machine Learning is Leading the Way
Why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data with machine learning.
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